---
title: 'the geodata package '
author: 'Jonathan Jupke '
date: '2021-09-17'
slug: the-geodata-package
categories:
- GIS
tags:
- GIS
- R package
comments: no
images: ~
---
The geodata package is written and maintained by Rob Hijmans who also wrote the raster and terra packages.
The purpose of the package is to provide an easy-to-use interface to download different handy spatial data sets directly from R.
In this script, I will go through most of the functions that this package provides, show you how to use them and what their outputs look like.
## Warning: Paket 'geodata' wurde unter R Version 4.1.1 erstellt
## Warning: Paket 'terra' wurde unter R Version 4.1.1 erstellt
cmip6
With this function, you can download climate projections from the Coupled Model Intercomparison Project 6 (CIMP6, see here for more information).
Shortly, CMIP is an multi-group project that compares the projection of different climate models and also combined them in so called ensemble models to project future climates.
The function has six arguments cmip6_world(model, ssp, time, var, res, path).
model determines which of the CIMP6 models you want to get the results from.
You can select between: “BCC-CSM2-MR”, “CNRMCM6-1”, “CNRM-ESM2-1”, “CanESM5”, “GFDL-ESM4”, “IPSL-CM6A-LR”, “MIROC-ES2L”, “MIROC6” and “MRI-ESM2-0”.
Each model represents the efforts of one working group namely: the Bejing Climate Center (BCC-CSM), the Centre National de Recherches Météorologiques (CNRMCM and CNRM-ESM); the Canadian Centre for Climate Modelling (CanESM); the Geophysical Fluid Dynamics Laboratory (GFDL-ESM4), the Institute Pierre Simon Laplace (IPSL), the Japan Agency for Marine-Earth Science and Technology (MIROC-ES2L and MIROC6) and lastly, the Meterological Research Insitute (MRI-ESM2-0).
ssp is the code for a “Shared Socio-economic Pathway” (see here and here). Possible codes are “126”, “245”, “370” and “585”. The first number here gives the general narrative which start at very sustainable (SSP1) and grow more and more fossil fuel dependend (SSP5).
time refers to the time period that you would like to have the results for? Possible intervals are: “2021-2040”, “2041-2060”, or “2061-2080”.
var determines which variable is returned? The climate models above return: minimum temperature (“tmin”), maximum temperature (“tmax”), average temperature (“tavg”), precipitation (“prec”) and biolcimatic variables (“bioc”, see here for an explanation).
res is the resolution of the raster that is returned. Valid values are 2.5, 5 and 10 (minutes of a degree).
Lastly, path refers to the folder you want to save the data in?
Enter the path to the designated folder here.
To demonstrate the functions capabilities, we will download the global monthly minum temperatures from the Chinese model, for the foreseeable future on a sustainable socioeconomic pathway, in a resolution of 10 minutes of a degree (i.e. 1/6th of a degree).
bcc.10.tmin <- geodata::cmip6_world(model = "BCC-CSM2-MR",
ssp = "126",
time = "2021-2040",
var = "tmin",
res = 10,
path = "geodata/")
From a quick look we can see that it is a SpatRaster with 12 layers (months of the year).
It is in longitude and latitude.
The code below uses the tmap package to save the 12 rasters as a animation in gif format, which is displayed underneath.
The country_codes() function is different from most other functions included in this package.
It does not download spatial data but instead provide a table with different codes and IDs for countries.
Below you can see the first few lines.
## NAME ISO3 ISO2 NAME_ISO
## 1 Afghanistan AFG AF AFGHANISTAN
## 2 Akrotiri and Dhekelia XAD <NA> <NA>
## 3 Åland ALA AX ÅLAND ISLANDS
## 4 Albania ALB AL ALBANIA
## 5 Algeria DZA DZ ALGERIA
## 6 American Samoa ASM AS AMERICAN SAMOA
elevation
The are three different elevation functions.
Each loads a digital elevation model (DEM, in this case from the Shuttle Radar Topography Mission, see here).
The three functions are elevation_3s(), elevation_30s() and elevation_global(). elevation_30s() has a country argument which you can supply the name or ISO3 code (see function country_code) and it will download the respective DEM.
As I am writing this, the elevation_3s() function does not seem to work, there is a problem with the server.
The elevation_global() function dowloads a dem for the entires globe for in a specified resolution.
Just as example, and because its relatively small, I downloaded the DEM for Andrroa.
You can explore it in the interactive map below.
## tmap mode set to interactive viewing
tm_shape(dem_andorra) + tm_raster()
## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1
The DEM for the whole world is to large to display interactively, so we will just plot it staticly.
plot(dem_global)
gadm
The function gadm() returns a SpatVector (the terra vector class) of administrative boundaries. It has 3 arguments. country is the country name or ISO3 code (see function country_code()).
level is the the administrative subdivision, the higher the level the more detail the map has.
For higher levels this is quite extensive.
To find Landau as an object in the data set, for example, you only need to go to level 3
Here we only look at level 0 (all of Germany) and level 1 (federal states).
world |> st_as_sf() |> st_make_valid() |> tm_shape() + tm_polygons()
On this map we see that the polygons were created on a plane. Geometries that cross the antimeridian (180° longitude) are stretched across the whole globe.
This is also the reason that we need to use st_make_valid() which makes to display the data.
This problem occurs because the sf package assumes spherical geometries.
We can explicitly tell it to not do so and avoid the ugly smears on the map.
sf::sf_use_s2(FALSE)
## Spherical geometry (s2) switched off
world |> st_as_sf() |> st_make_valid() |> tm_shape() + tm_polygons()
sp_occurrence
The sp_occurrence() function can be used to download data from the global biodiversity information facility (GBIF).
We provide the function with genus and species names we want to dowload observations for.
Here Bubo bubo, the Eagle Owl.
occ.bb <- geodata::sp_occurrence(genus = "Bubo", species = "bubo")
This data set has may columns and lots of observations.
To simplify things, we remove all columns except longitude and latitude (dplyr::select()) and only keep observations that have data in these columns (dplyr::filter()) and transform it into a simple feature geometry object.
